This section introduces aspects that may help facilitate a better understanding of the disclosure. Accordingly, these statements are to be read in this light and are not to be understood as admissions about what is or is not prior art.
Typically, a semiconductor switch is constructed from an arrangement that provides a deterministic output. For example, silicon-based switches behave in a manner such that when a predetermined input is provided, the output is deterministic (e.g., a high or a low). Nowadays, with an ever-challenging requirement generated by the explosion of computing demands, the typical semiconductor switches are failing to answer the call of data exchange in such applications as big-data, machine learning and artificial intelligence, Internet-of-things (IoT), and other data intensive operations. In response to these challenges, other technologies are now being investigated. One such technology is spin-torque transfer (STT)-based memory devices. Such STT-based devices typically use magnetic tunnel junction (MTJ) switches. These switches aim to alleviate the shortcomings of traditional switches such as power and cycle time.
In contrast to deterministic switching, in certain applications such as stochastic neural networks, it is desired to have a stochastic-probabilistic switching mechanism. One example of such an application is a tunable random number generator (tunable RNG) that can be used as a building block for building stochastic neural networks to be used for Machine Learning and Quantum Computing applications. However, the typical solutions for RNG applications are typically bulky and suffer from the same challenges discussed in typical computing circumstances.
Therefore, there is an unmet need for a novel approach to answer the challenges posed by situations in which stochastic and probabilistic switching mechanisms are needed.